def experiment(n,new_offset=0,s1="eh",s2="es",s3="nl",s4="mh"):
global offset,slots
if all(list(map(lambda x: isinstance(x,str),[s1,s2,s3,s4]))):
s1 = s1.lower()
s2 = s2.lower()
s3 = s3.lower()
s4 = s4.lower()
if s1 in [s2,s3,s4] or s2 in [s1,s3,s4] or s3 in [s1,s2,s4] or s4 in [s1, s2, s3]:
actr.print_warning("Duplicate slot names provided. Using default slots.")
slots = ["eh", "es", "nl", "mh"]
elif 'category' in [s1,s2,s3,s4]:
actr.print_warning("Slot named category cannot be used. Using default slots.")
slots = ["eh", "es", "nl", "mh"]
else:
slots=[s1,s2,s3,s4]
else:
actr.print_warning("Not all slot names provided are strings. Using default slots.")
slots = ["eh", "es", "nl", "mh"]
if isinstance(new_offset,numbers.Number):
offset = new_offset
else:
offset = 0
trials = len(cat_data)
results = [0] * trials
counts = [0] * trials
for i in range(n):
answers,responses = do_experiment()
results = list(map(lambda x,y: x + y,results,answers))
counts = list(map(lambda x,y: x + y,counts,responses))
results=list(map(lambda x: x/n,results))
offset = 0
actr.correlation(results,cat_data)
actr.mean_deviation(results,cat_data)
print("P(C=1)")
print(" ",end="")
for i in range(trials):
print("(%4d) " % counts[i],end="")
print()
print("data ",end="")
for i in range(trials):
print("%7.3f" % cat_data[i],end="")
print()
print("model ",end="")
for i in range(trials):
print("%7.3f" % results[i],end="")
print()
def print_results(predicted, data, label):
print()
print(label)
actr.correlation(predicted, data)
actr.mean_deviation(predicted, data)
print("Trial 1 2 3 4 5 6 7 8")
print(" ", end='')
for i in predicted:
print('%8.3f' % i, end='')
print()
def display_results(results):
questions = ["1+1","1+2","1+3","2+2","2+3","3+3"]
actr.correlation(results,siegler_data)
actr.mean_deviation(results,siegler_data)
print(" 0 1 2 3 4 5 6 7 8 Other")
for i in range(6):
print(questions[i],end="")
for j in range(10):
print("%6.2f" % results[i][j],end="")
print()
def compare(n):
rts = [0, 0, 0, 0, 0, 0, 0, 0, 0]
counts = [0, 0, 0, 0, 0, 0, 0, 0, 0]
for i in range(n):
r, c = experiment(False, False)
rts = list(map(lambda x, y: x + y, rts, r))
counts = list(map(lambda x, y: x + y, counts, c))
rts = list(map(lambda x: x / n, rts))
counts = list(map(lambda x: x / n, counts))
actr.correlation(rts, control_data)
actr.mean_deviation(rts, control_data)
print_analysis(rts, counts, [1, 2, 3], ['2', '3', '4'], [192, 192, 192])
def experiment(n, human=False):
l = len(test_stim)
result = [0] * l
p_values = [["decide-over", 0], ["decide-under", 0], ["force-over", 0],
["force-under", 0]]
for i in range(n):
actr.reset()
d = bst_set(human, vis=True, stims=test_stim)
for j in range(l):
if d[j] == "over":
result[j] += 1
actr.hide_output()
# for p in p_values:
# p[1] += production_u_value(p[0])
actr.unhide_output()
result = list(map(lambda x: 100 * x / n, result))
if len(result) == len(exp_data):
actr.correlation(result, exp_data)
actr.mean_deviation(result, exp_data)
print()
print("Trial ", end="")
for i in range(l):
print("%-8d" % (i + 1), end="")
print()
print(" ", end="")
for i in range(l):
print("%8.2f" % result[i], end="")
print()
print()
for p in p_values:
print("%-12s: %6.4f" % (p[0], p[1] / n))
def output_person_location(data):
rts = list(map(lambda x: x[0], data))
actr.correlation(rts, person_location_data)
actr.mean_deviation(rts, person_location_data)
print("\nTARGETS:\n Person fan")
print(" Location 1 2 3")
print(" fan")
for i in range(3):
print(" %d " % (i + 1), end="")
for j in range(3):
print("%6.3f (%-5s)" % (data[j + (i * 3)]), end="")
print()
print()
print("FOILS:")
for i in range(3):
print(" %d " % (i + 1), end="")
for j in range(3):
print("%6.3f (%-5s)" % (data[j + ((i + 3) * 3)]), end="")
print()
def report_data(data):
actr.correlation(data, exp_data)
actr.mean_deviation(data, exp_data)
print_results(data)